More information about the journal
Statistics and probability or data science concepts are an integral part of the New Zealand school curriculum and assessment, specifically in the mathematics and statistics curriculum, and implicitly in the science, physical education, social science curricula and any other school discipline where data are used. At the tertiary level, statistics, probability, modelling, and data science are taught at the undergraduate and graduate levels as disciplines in their own right. However, data and modelling or data science are ubiquitous across many disciplines (e.g. economics, engineering, linguistics, sociology) and are taught in situ. The Statistics and Data Science Educator seeks to provide free peer-reviewed teaching materials for teachers who develop statistical, probabilistic and modelling concepts in their classrooms.
Statistics and Data Science Educator lesson plans identify both the statistics, probability, and modelling concepts being developed and the age range appropriate for their use. The lessons are organised around a sequence of main instructional elements relevant to the topic and type of lesson design (e.g. Model Eliciting Activity, Statistical enquiry process PPDAC (problem, plan, data analysis, conclusion)). Supplementary materials such as data sets, videos, online learning and teaching tools, embedded media, embedded interactive, and digital tools are encouraged. Teachers can navigate Statistics and Data Science Educator lessons by age level and topic.
Statistics and Data Science Educator is published continuously with accepted lesson plans being published when ready. In October each year, an editorial and acknowledgement of reviewers will be published. Contact Statistics and Data Science Educator (statisticsdatascienceeducator@gmail.com) for further information.
Managing Editor Maxine Pfannkuch, The University of Auckland
Website and Technical Editor Anna Fergusson, The University of Auckland
Statistics and Data Science Educator is indebted to STEW (STatistics Education Web), its recent replacement the Statistics Teacher, and their founders, the American Statistical Association/National Council of Teachers of Mathematics Joint Committee, for sharing their material and ideas during the formation of this journal. Statistics and Data Science Educator also acknowledges the work of the Australian Curriculum Corporation Mathematics Curriculum and Teaching Program (MCTP) Chance and Data Investigations by Charles Lovitt and Ian Lowe, whose work has influenced the style of Statistics and Data Science Educator lesson plans.